Lagrangian wavelet analysis of turbulence modulation in particle–liquid mixing flows

Abstract

A new experimental-theoretical framework has been developed to investigate turbulence and turbulence modulation in a two-phase multi-component particle-liquid flow in a mechanically agitated vessel. A discrete wavelet transform is used to decompose long-term three-dimensional Lagrangian trajectories of flow phases, acquired by a technique of positron emission particle tracking, into their deterministic and stochastic sub-trajectories. The sub-trajectories are then used to construct the different-scale local velocity and turbulent kinetic energy fields of the two-phase flow. The effects of particle size and size distribution mode (mono, binary and polydisperse), particle concentration, impeller agitation speed and pumping mode on turbulence intensity are investigated. Amongst these factors, particle size, impeller pumping mode and particle size distribution mode have a significant impact on liquid turbulence. The presence of large particles enhances liquid turbulence and broadens the region in the vessel characterised by high local turbulent kinetic energy (TKE) values. Results also show that a down-pumping pitched blade turbine generates significantly greater local maxima in the TKE field, which tend to be more localized in the impeller discharge stream. In addition, binary or polydisperse suspensions containing higher fractions of larger particles produce higher turbulence intensities in the carrier phase. The detailed information obtained on turbulence intensity is crucial for a better understanding of the dynamics of particle-liquid flows inside mixing vessels to aid the rational design of these units.

Experimentally trained hybrid machine learning algorithm for predicting turbulent particle-laden flows in pipes

“…Thus, various approaches have been reported to analyze such Lagrangian trajectories and extract useful flow information. [29][30][31] Additionally, to consider the effects of both particle and liquid velocity fluctuations while simplifying the problem, the combination of a dominant mean velocity with a stochastic fluctuation has been widely used to approximate the dynamics of particleliquid flow systems. 32 In this work, the hybrid ML algorithm approach is used to analyze turbulent particle transport in a horizontal pipe, due to its simplicity, straightforward implementation, and robustness for classification as well as regression.…”